This is the first article in a 4-part series that will explore how to expand new opportunities by leveraging a rest-based API environment to create consumable data from advanced analytics. Topics will include the (1) current state of many organizations today, (2) things to keep in mind when building an advanced analytics environment (Data Lake), (3) considerations for an effective API approach, and (4) how it all comes together.
During a recent engagement, I was talking with several company IT leaders who were heavily focused on the implementation of their enterprise API program. The conversation turned to Big Data and the potential for new business opportunities driven by advanced analytics. As we talked, I began to realize that their focus was entirely on how to transition their existing services from old to new. They were aware but had not yet considered how an enterprise API program combined with advanced analytics could open and expand potentially new markets. This helped solidify my own thoughts having recently come from building an enterprise big data program for a fortune 50 company. From the analytics perspective, we had no problem. The problem came in how to make consistent information easily available to a broad audience.
Analytics has become something all of us interact with every day, whether we realize it or not. The same goes for API’s. Almost any interaction you have on any connected device is API driven. It is the backbone of how the digital world communicates. Many businesses have figured ways to leverage both API’s and analytics and create new business models in the process. Have you booked a trip recently? If you have and utilized a discount travel provider, you took advantage of both analytics and API’s. Many more traditional businesses are just now beginning to understand the possibilities and still others are overlooking the opportunities entirely. Analytics can help you identify options, but then what? How can your company leverage the data and analytics you currently generate to create new business opportunities and reach a broader audience?
Many organizations have built applications over the years that look something like the picture below. As the business has grown, individual teams are tasked with finding creative and targeted solutions for a given customer or problem. Analytics are focused on one solution and data is siloed for that single solution. Over time, the ability to manage, maintain and adapt these applications to the changing needs of the business weighs down an organization to the point that it becomes impossible to keep up. Changes take longer and significantly more resources. Data becomes inconsistent and/or incorrect. No one knows where the true master data source resides. Expert resources that designed and understood the applications move on and leave knowledge gaps. If given enough time, these situations can potentially damage the brand, the company or both.
This is not a unique situation and one that many businesses find themselves in today. The reality is, there are two problems that exist in this situation: (1) the analytics/data problem, which involves how data is managed and maintained for analytics, and (2) the consumption problem. The solution to the consumption problem, typically founded in APIs, directly drives the relationship between your business and your customers (internal or external). How easy is it to gain secure access to data that is consistent and accurate? Are you providing information in such a way that your customers want to tell everyone about your solutions? In many companies, the analytics/data and API (consumption) solutions are tackled independently. The technologies are different, the implementation details are different and, in many cases, the teams never cross paths. I would maintain that to be a truly world class organization that leverages analytics to its maximum, the communication lines across both API (consumption) and analytics/data teams must be broad and deep. Analytics defines new opportunities and API’s help turn those opportunities into reality.
In the next article we will dive into some proven suggestions for how to effectively build a Data Lake.